计算机科学 ›› 2014, Vol. 41 ›› Issue (4): 252-255.

• 人工智能 • 上一篇    下一篇

基于神经网络改进的云环境下暴发式请求部署策略研究

陈鹏,马自堂,孙磊,孙冬冬   

  1. 解放军信息工程大学三院 郑州450004;解放军信息工程大学三院 郑州450004;解放军信息工程大学三院 郑州450004;61579部队 北京102400
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受武器装备预研重点基金资助

Deployment Strategies Research on Cloud Computing under Bursty Workloads on Neural Network

CHEN Peng,MA Zi-tang,SUN Lei and SUN Dong-dong   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对暴发式任务请求给云计算系统性能带来的影响,结合现有资源部署模型,提出了一种基于误差反向传播神经网络改进的资源部署模型来应对上述问题。模型判断出暴发式任务请求的始末时,自动启动网络模块,通过事先训练好的网络进行参数调整值的预测,以达到动态跟踪云计算系统底层资源与外界任务请求变化的目的。通过CloudSim对模型进行了仿真实验,结果证明,引入神经网络模块可有效提高现有系统的资源部署响应速度。

关键词: 云计算,神经网络,资源部署,暴发式任务请求

Abstract: Aiming at the degrading system performance that bursty workloads bring in cloud computing,a resource deployment model based on error back-propagation neural network was proposed to resolve the problems referred to above.A network module is started automatically when the beginning of bursty workloads is judeged.The prediction of parameter adjustment value is carried out by using pre-trained network to achieve the purpose of tracking dynamically the changing of underlying resource and outside world task in cloud computing system.The results of simulation in CloudSim prove that the response speed of resource deployment can be improved efficiently by bringing neural network module.

Key words: Cloud computing,Neural network,Resource allocation,Bursty workloads

[1] 文雨,孟丹,詹剑锋.面向应用服务级目标的虚拟化资源管理[J].软件学报,2013,4(2):358-377
[2] Caniff A,Lu Lei,Mi Ning-fang,et al.Fastrack for TamingBurstiness and Saving Power in Multi-Tiered Systems[C]∥22nd International Teletraffic Congress(ITC 22).Amsterdam,the Netherlands,September 2010
[3] Tai Jiang-zhe,Meleis W,Zhang Jue-min,et al.ARA:AdaptiveResource Allocation for Cloud Computing Environments under Bursty Workloads,978-1-4673[R].Northeastern University,Boston,USA,2011
[4] 高刃,唐龙,伍爵博.基于神经网络的无线传感器网络数据预测应用研究[J].计算机科学,2012,0(5):44-47
[5] 马锐.人工神经网络原理[M].北京:机械工业出版社,2010
[6] Tirado J M,Higuero D,Isaila F,et al.Predictive Data Grouping and Placement for Cloud-based Elastic Server Infrastructures[C]∥201111th IEEE/ACM International Symposium on Cluster,Cloud and Grid Computing.IEEE DOI/CCGrid,2011:285-294
[7] C12G Labs S.L.Private cloud computing with OpenNebula 1.4[EB/OL].http://opennebula.org/_media/software:ecosystem:private_cloud_computing_with_opennebula_1.4.pdf,2010
[8] 刘进军,赵生慧.面向云计算的多虚拟机管理模型的设计[J].计算机应用,2011,31(5):1417-1419
[9] Arlitt M,Jin T.Workload characterization of the 1998World Cup Web site[R].HPL-1999-35R1.HP Laboratories,1999
[10] 李强,郝沁汾,肖利民,等.云计算中虚拟机放置的自适应管理与多目标优化[J].计算机学报,2011,4(12):2253-2264
[11] Rodrigo N C,Ranjan R,Beloglazov A,et al.CloudSim:A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms[R].Cloud Computing and Distributed Systems Laboratory,Australia,2010

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